Determination of Antiepileptic Drugs Withdrawal Through EEG Hjorth Parameter Analysis.
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| Title: | Determination of Antiepileptic Drugs Withdrawal Through EEG Hjorth Parameter Analysis. |
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| Authors: | Ouyang, Chen-Sen1 (AUTHOR), Yang, Rei-Cheng2 (AUTHOR), Wu, Rong-Ching3 (AUTHOR), Chiang, Ching-Tai4 (AUTHOR), Lin, Lung-Chang5 (AUTHOR) lclin@kmu.edu.tw |
| Source: | International Journal of Neural Systems. Nov2020, Vol. 30 Issue 11, pN.PAG-N.PAG. 16p. |
| Subjects: | Anticonvulsants, Automated external defibrillation, People with epilepsy, Seizures (Medicine), Electroencephalography, Physicians, Brain-computer interfaces |
| Abstract: | The decision to continue or to stop antiepileptic drug (AED) treatment in patients with prolonged seizure remission is a critical issue. Previous studies have used certain risk factors or electroencephalogram (EEG) findings to predict seizure recurrence after the withdrawal of AEDs. However, validated biomarkers to guide the withdrawal of AEDs are lacking. In this study, we used quantitative EEG analysis to establish a method for predicting seizure recurrence after the withdrawal of AEDs. A total of 34 patients with epilepsy were divided into two groups, 17 patients in the recurrence group and the other 17 patients in the nonrecurrence group. All patients were seizure free for at least two years. Before AED withdrawal, an EEG was performed for each patient that showed no epileptiform discharges. These EEG recordings were classified using Hjorth parameter-based EEG features. We found that the Hjorth complexity values were higher in patients in the recurrence group than in the nonrecurrence group. The extreme gradient boosting classification method achieved the highest performance in terms of accuracy, area under the curve, sensitivity, and specificity (84.76%, 88.77%, 89.67%, and 80.47%, respectively). Our proposed method is a promising tool to help physicians determine AED withdrawal for seizure-free patients. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Neural Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 146703992 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Determination of Antiepileptic Drugs Withdrawal Through EEG Hjorth Parameter Analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ouyang%2C+Chen-Sen%22">Ouyang, Chen-Sen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Rei-Cheng%22">Yang, Rei-Cheng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Rong-Ching%22">Wu, Rong-Ching</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chiang%2C+Ching-Tai%22">Chiang, Ching-Tai</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Lung-Chang%22">Lin, Lung-Chang</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> lclin@kmu.edu.tw</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Neural+Systems%22">International Journal of Neural Systems</searchLink>. Nov2020, Vol. 30 Issue 11, pN.PAG-N.PAG. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Anticonvulsants%22">Anticonvulsants</searchLink><br /><searchLink fieldCode="DE" term="%22Automated+external+defibrillation%22">Automated external defibrillation</searchLink><br /><searchLink fieldCode="DE" term="%22People+with+epilepsy%22">People with epilepsy</searchLink><br /><searchLink fieldCode="DE" term="%22Seizures+%28Medicine%29%22">Seizures (Medicine)</searchLink><br /><searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Physicians%22">Physicians</searchLink><br /><searchLink fieldCode="DE" term="%22Brain-computer+interfaces%22">Brain-computer interfaces</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The decision to continue or to stop antiepileptic drug (AED) treatment in patients with prolonged seizure remission is a critical issue. Previous studies have used certain risk factors or electroencephalogram (EEG) findings to predict seizure recurrence after the withdrawal of AEDs. However, validated biomarkers to guide the withdrawal of AEDs are lacking. In this study, we used quantitative EEG analysis to establish a method for predicting seizure recurrence after the withdrawal of AEDs. A total of 34 patients with epilepsy were divided into two groups, 17 patients in the recurrence group and the other 17 patients in the nonrecurrence group. All patients were seizure free for at least two years. Before AED withdrawal, an EEG was performed for each patient that showed no epileptiform discharges. These EEG recordings were classified using Hjorth parameter-based EEG features. We found that the Hjorth complexity values were higher in patients in the recurrence group than in the nonrecurrence group. The extreme gradient boosting classification method achieved the highest performance in terms of accuracy, area under the curve, sensitivity, and specificity (84.76%, 88.77%, 89.67%, and 80.47%, respectively). Our proposed method is a promising tool to help physicians determine AED withdrawal for seizure-free patients. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Neural Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1142/S0129065720500367 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: N.PAG Subjects: – SubjectFull: Anticonvulsants Type: general – SubjectFull: Automated external defibrillation Type: general – SubjectFull: People with epilepsy Type: general – SubjectFull: Seizures (Medicine) Type: general – SubjectFull: Electroencephalography Type: general – SubjectFull: Physicians Type: general – SubjectFull: Brain-computer interfaces Type: general Titles: – TitleFull: Determination of Antiepileptic Drugs Withdrawal Through EEG Hjorth Parameter Analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ouyang, Chen-Sen – PersonEntity: Name: NameFull: Yang, Rei-Cheng – PersonEntity: Name: NameFull: Wu, Rong-Ching – PersonEntity: Name: NameFull: Chiang, Ching-Tai – PersonEntity: Name: NameFull: Lin, Lung-Chang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 01290657 Numbering: – Type: volume Value: 30 – Type: issue Value: 11 Titles: – TitleFull: International Journal of Neural Systems Type: main |
| ResultId | 1 |
